System and method for automatically retraining machine learning models to predict bias in a data input

ABSTRACT

A method includes receiving, by a processor, bias data categories. A data input from a user for classification in data categories is received. A classification machine learning model is utilized to classify the data input in at least one data category and determine a first confidence probability in a classification outcome. A bias filter machine learning model is utilized to determine a second confidence probability that the classification outcome of classifying the data input into the at least one data category is based on at least one bias characteristic associated with at least one bias data category. A gate machine learning model is utilized to determine when to output the classification outcome of classifying the data input into the at least one data category to a computing device of a user based at least in part on the first confidence probability, the second confidence probability, and a predefined bias threshold.

COPYRIGHT NOTICE

A portion of the disclosure of this patent document contains materialthat is subject to copyright protection. The copyright owner has noobjection to the facsimile reproduction by anyone of the patent documentor the patent disclosure, as it appears in the Patent and TrademarkOffice patent files or records, but otherwise reserves all copyrightrights whatsoever. The following notice applies to the software and dataas described below and in drawings that form a part of this document:Copyright, Capital One Services, LLC., All Rights Reserved.

FIELD OF TECHNOLOGY

The present disclosure generally relates to improved computer-basedsystems and methods for managing classification outcomes of data inputsclassified into bias categories.

BACKGROUND OF TECHNOLOGY

A computer network platform/system may include a group of computers(e.g., clients, servers, smart routers) and other computing hardwaredevices that are linked together through one or more communicationchannels to facilitate communication and/or resource-sharing, via one ormore specifically programmed graphical user interfaces (GUIs) of thepresent disclosure, among a wide range of users.

SUMMARY OF DESCRIBED SUBJECT MATTER

In some embodiments, the present disclosure provides an exemplarytechnically improved computer-based method that includes at least thefollowing steps of:

receiving, by a processor, a plurality of bias data categories;

receiving, by the processor, a data input from a user for classificationin a plurality of data categories;

utilizing, by the processor, a classification machine learning model toclassify the data input in at least one data category from the pluralityof data categories and determine a first confidence probability in aclassification outcome;

utilizing, by the processor, a bias filter machine learning model todetermine a second confidence probability that the classificationoutcome of classifying the data input into the at least one datacategory from the plurality of data categories is based on at least onebias characteristic associated with at least one bias data category fromthe plurality of bias data categories;

utilizing, by the processor, a gate machine learning model to determinewhen to output the classification outcome of classifying the data inputinto the at least one data category from the plurality of datacategories to a computing device of a user based at least in part on:

i) the first confidence probability,

ii) the second confidence probability, and

iii) a predefined bias threshold; and

outputting, by the processor, the classification outcome of classifyingthe data input into the at least one data category from the plurality ofdata categories to the computing device of the user based on thedetermination of the gate machine learning model.

In some embodiments, the present disclosure provides an exemplarytechnically improved computer-based system that includes at least thefollowing components of a system may include a memory and a processor.The processor may be configured to:

receive a plurality of bias data categories;

receive a data input from a user for classification in a plurality ofdata categories;

utilize a classification machine learning model to classify the datainput in at least one data category from the plurality of datacategories and determine a first confidence probability in aclassification outcome;

utilize a bias filter machine learning model to determine a secondconfidence probability that the classification outcome of classifyingthe data input into the at least one data category from the plurality ofdata categories is based on at least one bias characteristic associatedwith at least one bias data category from the plurality of bias datacategories;

utilize a gate machine learning model to determine when to output theclassification outcome of classifying the data input into the at leastone data category from the plurality of data categories to a computingdevice of a user based at least in part on:

-   i) the first confidence probability,-   ii) the second confidence probability, and-   iii) a predefined bias threshold; and

output the classification outcome of classifying the data input into theat least one data category from the plurality of data categories to thecomputing device of the user based on the determination of the gatemachine learning model.

BRIEF DESCRIPTION OF THE DRAWINGS

Various embodiments of the present disclosure can be further explainedwith reference to the attached drawings, wherein like structures arereferred to by like numerals throughout the several views. The drawingsshown are not necessarily to scale, with emphasis instead generallybeing placed upon illustrating the principles of the present disclosure.Therefore, specific structural and functional details disclosed hereinare not to be interpreted as limiting, but merely as a representativebasis for teaching one skilled in the art to variously employ one ormore illustrative embodiments.

FIG. 1 depicts a first block diagram of an exemplary computer-basedsystem for managing classification outcomes of data inputs classifiedinto bias categories, in accordance with one or more embodiments of thepresent disclosure;

FIGS. 2A-2C depict exemplary embodiments of different data inputs andtheir classification outcomes, in accordance with one or moreembodiments of the present disclosure;

FIG. 3 depicts a second block diagram of an exemplary computer-basedsystem for managing classification outcomes of data inputs classifiedinto bias categories, in accordance with one or more embodiments of thepresent disclosure;

FIG. 4 depicts various classification outcomes of data inputs classifiedinto bias categories, in accordance with one or more embodiments of thepresent disclosure;

FIG. 5 illustrates a flowchart of an exemplary inventive method formanaging classification outcomes of data inputs classified into biascategories, in accordance with one or more embodiments of the presentdisclosure;

FIG. 6 depicts a block diagram of an exemplary computer-basedsystem/platform in accordance with one or more embodiments of thepresent disclosure;

FIG. 7 depicts a block diagram of another exemplary computer-basedsystem/platform accordance with one or more embodiments of the presentdisclosure; and

FIGS. 8 and 9 are diagrams illustrating implementations of cloudcomputing architecture/aspects with respect to which the disclosedtechnology may be specifically configured to operate, in accordance withone or more embodiments of the present disclosure.

DETAILED DESCRIPTION

Various detailed embodiments of the present disclosure, taken inconjunction with the accompanying figures, are disclosed herein;however, it is to be understood that the disclosed embodiments aremerely illustrative. In addition, each of the examples given inconnection with the various embodiments of the present disclosure isintended to be illustrative, and not restrictive.

Throughout the specification, the following terms take the meaningsexplicitly associated herein, unless the context clearly dictatesotherwise. The phrases “in one embodiment” and “in some embodiments” asused herein do not necessarily refer to the same embodiment(s), thoughit may. Furthermore, the phrases “in another embodiment” and “in someother embodiments” as used herein do not necessarily refer to adifferent embodiment, although it may. Thus, as described below, variousembodiments may be readily combined, without departing from the scope orspirit of the present disclosure.

In addition, the term “based on” is not exclusive and allows for beingbased on additional factors not described, unless the context clearlydictates otherwise. In addition, throughout the specification, themeaning of “a,” “an,” and “the” include plural references. The meaningof “in” includes “in” and “on.”

It is understood that at least one aspect/functionality of variousembodiments described herein can be performed in real-time and/ordynamically. As used herein, the term “real-time” is directed to anevent/action that can occur instantaneously or almost instantaneously intime when another event/action has occurred. For example, the “real-timeprocessing,” “real-time computation,” and “real-time execution” allpertain to the performance of a computation during the actual time thatthe related physical process (e.g., a user interacting with anapplication on a mobile device) occurs, in order that results of thecomputation can be used in guiding the physical process.

As used herein, the term “dynamically” and term “automatically,” andtheir logical and/or linguistic relatives and/or derivatives, mean thatcertain events and/or actions can be triggered and/or occur without anyhuman intervention. In some embodiments, events and/or actions inaccordance with the present disclosure can be in real-time and/or basedon a predetermined periodicity of at least one of: nanosecond, severalnanoseconds, millisecond, several milliseconds, second, several seconds,minute, several minutes, hourly, several hours, daily, several days,weekly, monthly, etc.

As used herein, the term “runtime” corresponds to any behavior that isdynamically determined during an execution of a software application orat least a portion of software application.

Embodiments of the present disclosure teach methods and systems formanaging classification outcomes of data inputs classified into biascategories. Classification machine learning models may be configured toreceive a data input, such as image data of a picture, and classify whatobject, animate or inanimate, is displayed in the image data intoclassification categories. Similarly, the data input may include data ofa voice recording where the classification machine learning algorithmsmay identify a person speaking or an accent of the person's voice in thevoice recording. However, many times the classification machine learningalgorithm may classify the data input incorrectly, which can be veryproblematic if there is an offensive misclassification, such as a humanbeing classified as a gorilla, for example.

In some embodiments, a bias category may be defined at least in part onone or more biases associated with gender, race, skin color, shape ofbody part, mental abilities, physical abilities, place of origin, socialaffiliation, political affiliation, education level, for example.

In some embodiments, both the classification machine learning model anda bias filter machine learning model may receive the same data input.The bias filter machine learning model may be used to determine aprobability that the data input may be classified into a category thatmay be biased. In the context of the present disclosure, a biasedclassification may be potentially offensive or may cause damage topeople or an entity if the data input is misclassified and/ormisinterpreted. The output of the classification machine learning modeland the bias filter machine learning model may then be relayed to a gatemachine learning model, which is configured to manage classificationoutcomes of the data inputs when classified into bias categories.

The exemplary embodiments shown hereinbelow solve the technical problemof misclassifying data inputs into bias data categories by the use of agate machine learning model to manage bias classification outcomeseither by blocking and/or warning users that the data inputclassifications may be biased.

FIG. 1 depicts a block diagram of an exemplary computer-based system 10for managing classification outcomes of data inputs classified into biascategories, in accordance with one or more embodiments of the presentdisclosure. System 10 may include a plurality of computing devices 15,such as servers, denoted server 15A, server 15B, and server 15C, as wellas a computer 25 including a display 27 of a user 30 all communicatingover a communication network 20. Each of servers 15 as shown in theenlargement for server 15A may include a processor 35, a memory 40,communication circuitry and interface 45, and input/output (I/O) devices50.

In some embodiments, processor 35 may be configured to execute code forsoftware modules of a classification machine learning model 55, a biasfilter machine learning model 60, and/or a gate machine learning model65. Memory 40 may be configured to store a list of data categories 70for use by classification machine learning model 55 and/or a list ofbias data categories 75 for use by bias filter machine learning model60.

FIGS. 2A-2C depict exemplary embodiments 100 of different data inputsand their classification outcomes, in accordance with one or moreembodiments of the present disclosure.

FIG. 2A depicts a first exemplary embodiment of image data inputs andtheir classification outcomes, in accordance with one or moreembodiments of the present disclosure. Each exemplary embodiment depictsdata categories 105 that data inputs 110 may be classified byclassification machine learning model (MLM) 55. MLM 55 may output aclassification outcome 115. For example, data categories 120 may be anelephant or a cat. For an image data input of an elephant 122, theclassification outcome may be an “elephant” 128 with a confidence level130 of 95%. For an image data input of a cat 124, the classificationoutcome may be a “cat” 132 with a confidence level 134 of 97%. For animage data input of a mouse 126, classifier MLM 55 may not determineclassification outcome 115 so classifier MLM 55 may report an output 135as “unknown”.

FIG. 2B depicts a second exemplary embodiment of a data input of a voiceclip 142 and their classification outcomes, in accordance with one ormore embodiments of the present disclosure. Voice clip 142 may be in anysuitable audio file input format such as an MPEG Audio Layer-3 (MP3),for example. Data categories 140 may include a “northern accent” or a“southern accent” for classifying the data input using classifier MLM55, which may recognize a voice of a Southerner 144 in voice clip 142with a confidence level 146 of 92%, for example.

FIG. 2C depicts a third exemplary embodiment of an image data input andtheir classification outcomes, in accordance with one or moreembodiments of the present disclosure. Data categories 150 may be aCaucasian male or female, a Non-Caucasian male or female, and animals.For an image data input of a Caucasian male 155, the classificationoutcome may be a “Caucasian male” 170 with a confidence level 172 of95%. For an image data input of an Non-Caucasian male 160, theclassification outcome may be a “Non-Caucasian male” 175 with aconfidence level 177 of 97%. For an image data input of a white gorilla165, classifier MLM 55 may misclassify the image data and report“Caucasian Male” 180 with a lower confidence level 182 of 67%. This is abias category 185.

Similarly, consider another exemplary scenario, for example, for apicture of four people—two of which are Non-Caucasian males and two ofwhich are Caucasian males. The input image data of this picture is thenrelayed to classifier MLM 55. If classifier MLM 55 reports any of thefour people in the picture as being a gorilla, this may be veryoffensive to the person classified as the gorilla. Accordingly, any ofthese data categories 150 may be bias categories when used tomisclassify the input data. Thus, embodiments of the present disclosuremay be used to manage the classification outcomes of data inputsclassified into bias categories by classifier MLM 55.

Using the second exemplary embodiment described above, a southern accentmay be a bias category in the following exemplary scenario. Supposethere is a catastrophic hurricane in the southern United States causinglarge financial losses in property damage. A person may call in the nextday after the hurricane to a financial institution applying for a loanor an increase in a credit card line of credit on a credit card, forexample. The telephone communication line may sample the voice of theapplicant. The voice data sample may then be relayed as an input toclassifier MLM 55 identifying the person as a southerner.

The agent of the financial institution and/or algorithms assessingwhether the applicant may be qualified to receive a loan and/or anincrease in a line of credit is biased. If the applicant is indeed avictim of the hurricane, the applicant may have severe financial lossesfrom the hurricane and the financial institution may use thisinformation in credit risk assessment. However, if the applicant ismerely a southerner living in the Northern United States, then thefinancial institution may mistakenly assume that the applicant is underfinancial stress. The financial institution may deny the applicant as acredit risk merely based on the applicant's southern accent and applyingfor more credit (e.g., loan and/or increased credit card line) one dayafter the hurricane devastation, for example.

In a similar vein for determining creditworthiness of an applicant forcredit in the same exemplary example above, classifier MLM 55 may usehandwriting sample and/or e-mail document written by the applicant toclassify the applicant as someone with higher education or lowereducation. For example, classifier MLM 55 may assess that the applicantwrites like a lawyer or a janitor to determine creditworthiness.However, the applicant may be a janitor writing like a lawyer or alawyer writing like a janitor so the assessed occupation of theapplicant may be a bias category.

FIG. 3 depicts a second block diagram of an exemplary computer-basedsystem 200 for managing classification outcomes of data inputsclassified into bias categories, in accordance with one or moreembodiments of the present disclosure. Processor 35 may perform thefunctions of system 200. Processor 35 may receive a data input 205 fromuser 30. Data input 205 may be a text file, an audio file, a video datafile, and/or an image data file.

Processor 35 may relay data input 205 to the inputs of bothclassification machine learning model 55 and bias filter machinelearning model 60. Classification machine learning model 55 may classifydata input 205 in at least one data category from a plurality of datacategories and may determine a first confidence probability (e.g.confidence level 172, confidence level 177, and/or confidence level 182)in a classification outcome.

Similarly, bias filter machine learning model 60 may determine a secondconfidence probability that the classification outcome of classifyingthe data input into the at least one data category from the plurality ofdata categories is based on at least one bias characteristic associatedwith at least one bias data category from the plurality of bias datacategories.

The data input, at least one data category, the at least one bias datacategory, as well as the first and second confidence probabilities maybe input to gate machine learning model 65 to determine how to manage aclassification outcome 210 of classifying the data input into the atleast one data category from the plurality of data categories to acomputing device of a user based at least in part on the firstconfidence probability, the second confidence probability, and apredefined bias threshold.

FIG. 4 depicts various classification outcomes 250 of data inputsclassified into bias categories, in accordance with one or moreembodiments of the present disclosure. Classification outcomes 250 maybe displayed on display 27 to user 30 such as on a graphic userinterface (GUI). Classification outcomes 250 may include data input 270,classified data categories 275 from classification machine learningmodel 55, a classification confidence probability (CPROB) 272 fromclassification machine learning model 55, and a bias confidenceprobability (BPROB) 274 from bias filter machine learning model 60 foreach of classified data categories 275.

In a first classification outcome 255, CPROB may be determined, forexample, to be 85% indicating high confidence in classifying the datainput, and BPROB may be determined to be, for example, 10% indicating alow bias probability. In this exemplary embodiment, processor 35 maycause to display the classified data categories 275 for data input 270on display 27 to user 30.

In a second classification outcome 260, CPROB may be determined, forexample, to be 47% indicating a relatively low confidence in classifyingdata input 270, and BPROB may be determined to be, for example, 89%indicating a very high bias probability. In this exemplary embodiment,processor 35 may block displaying classified data categories 275 fordata input 270 for display to user 30 on display 27 since theprobability of bias and misclassification are both high.

In a third classification outcome 265, CPROB may be determined, forexample, to be 75% indicating a relatively high confidence inclassifying data input 270, and BPROB may be determined to be, forexample, 56% indicating a moderate bias probability. In this exemplaryembodiment, processor 35 may cause to display classified data categories275 for data input 270 on display 27 to user 30 with an alertnotification 280 that there may be potential bias in the classified datacategories. Note that processor 35 may apply two predefined thresholdsto each of the two probabilities for assessing high confidence levels inclassification outcome and similarly, high bias confidence levels inassessing high bias confidence levels in the classification outcome.

In some embodiments, processor 35 may identify the classified data inputand the at least one data category as potentially biased may be used toretrain the at least one classification machine learning model.

In some embodiments, the bias filter machine learning model may beimplemented as, for example, a convolutional neural network model, adilated convolutional neural network model, a dense network, a recurrentneural network model, and/or any suitable neural network model. Thesemodels may be combined with autoencoder and/or variational autoencoderto better capture the features of samples causing bias.

In some embodiments, the classification machine learning model may beimplemented a classification neural network model, for example.

In some embodiments, the gate machine learning model may be implementedas a neural network model, for example.

In some embodiments, processor 35 may receive the plurality of bias datacategories from the user, from a regulator, or both. The regulators, forexample, from a regulatory body may defined protected classes of datacategories in the classification of the data inputs in accordance withthe embodiments taught herein.

Once the bias categories and confidence level thresholds (e.g.,classification and/or bias confidence level thresholds) are defined, theexemplary embodiments taught herein may be applied to any suitableclassification scenario using a classifier machine learning model whichmay result in bias.

In some embodiments, the exemplary inventive computer-basedsystems/platforms, the exemplary inventive computer-based devices,and/or the exemplary inventive computer-based components of the presentdisclosure may be configured to utilize one or more exemplary AI/machinelearning techniques chosen from, but not limited to, decision trees,boosting, support-vector machines, neural networks, nearest neighboralgorithms, Naive Bayes, bagging, random forests, and the like. In someembodiments and, optionally, in combination of any embodiment describedabove or below, an exemplary neutral network technique may be one of,without limitation, feedforward neural network, radial basis functionnetwork, recurrent neural network, convolutional network (e.g., U-net)or other suitable network. In some embodiments and, optionally, incombination of any embodiment described above or below, an exemplaryimplementation of Neural Network may be executed as follows:

-   i) Define Neural Network architecture/model,-   ii) Transfer the input data to the exemplary neural network model,-   iii) Train the exemplary model incrementally,-   iv) determine the accuracy for a specific number of timesteps,-   v) apply the exemplary trained model to process the newly-received    input data,-   vi) optionally and in parallel, continue to train the exemplary    trained model with a predetermined periodicity.

In some embodiments, bias filter machine learning model 60 may include aset of rules (e.g., bias terms/features).

In some embodiments, feedback may be provided to bias filter machinelearning model 60 where a media data input was incorrectly classified(e.g., labeled as sample(s) classified with bias).

In some embodiments and, optionally, in combination of any embodimentdescribed above or below, the exemplary trained neural network model mayspecify a neural network by at least a neural network topology, a seriesof activation functions, and connection weights. For example, thetopology of a neural network may include a configuration of nodes of theneural network and connections between such nodes. In some embodimentsand, optionally, in combination of any embodiment described above orbelow, the exemplary trained neural network model may also be specifiedto include other parameters, including but not limited to, biasvalues/functions and/or aggregation functions. For example, anactivation function of a node may be a step function, sine function,continuous or piecewise linear function, sigmoid function, hyperbolictangent function, or other type of mathematical function that representsa threshold at which the node is activated. In some embodiments and,optionally, in combination of any embodiment described above or below,the exemplary aggregation function may be a mathematical function thatcombines (e.g., sum, product, etc.) input signals to the node. In someembodiments and, optionally, in combination of any embodiment describedabove or below, an output of the exemplary aggregation function may beused as input to the exemplary activation function. In some embodimentsand, optionally, in combination of any embodiment described above orbelow, the bias may be a constant value or function that may be used bythe aggregation function and/or the activation function to make the nodemore or less likely to be activated.

FIG. 5 illustrates a flowchart of an exemplary inventive method formanaging classification outcomes of data inputs classified into biascategories, in accordance with one or more embodiments of the presentdisclosure. Method 300 may be performed by processor 35.

Method 300 may include receiving 305 a plurality of bias datacategories. Method 300 may include receiving 310 a data input from auser for classification in a plurality of data categories.

Method 300 may include utilizing 315 a classification machine learningmodel to classify the data input in at least one data category from theplurality of data categories and determine a first confidenceprobability in a classification outcome.

Method 300 may include utilizing 320 a bias filter machine learningmodel to determine a second confidence probability that theclassification outcome of classifying the data input into the at leastone data category from the plurality of data categories is based on atleast one bias characteristic associated with at least one bias datacategory from the plurality of bias data categories.

Method 300 may include utilizing 325 a gate machine learning model todetermine when to output the classification outcome of classifying thedata input into the at least one data category from the plurality ofdata categories to a computing device of a user based at least in parton: the first confidence probability, the second confidence probability,and a predefined bias threshold.

Method 300 may include outputting 330 the classification outcome ofclassifying the data input into the at least one data category from theplurality of data categories to the computing device of the user basedon the determination of the gate machine learning model.

As used herein, the terms “computer engine” and “engine” identify atleast one software component and/or a combination of at least onesoftware component and at least one hardware component which aredesigned/programmed/configured to manage/control other software and/orhardware components (such as the libraries, software development kits(SDKs), objects, etc.).

Examples of hardware elements may include processors, microprocessors,circuits, circuit elements (e.g., transistors, resistors, capacitors,inductors, and so forth), integrated circuits, application specificintegrated circuits (ASIC), programmable logic devices (PLD), digitalsignal processors (DSP), field programmable gate array (FPGA), logicgates, registers, semiconductor device, chips, microchips, chip sets,and so forth. In some embodiments, the one or more processors may beimplemented as a Complex Instruction Set Computer (CISC) or ReducedInstruction Set Computer (RISC) processors; x86 instruction setcompatible processors, multi-core, or any other microprocessor orcentral processing unit (CPU). In various implementations, the one ormore processors may be dual-core processor(s), dual-core mobileprocessor(s), and so forth.

Computer-related systems, computer systems, and systems, as used herein,include any combination of hardware and software. Examples of softwaremay include software components, operating system software, middleware,firmware, software modules, routines, subroutines, functions, methods,procedures, software interfaces, application program interfaces (API),instruction sets, computer code, computer code segments, words, values,symbols, or any combination thereof. Determining whether an embodimentis implemented using hardware elements and/or software elements may varyin accordance with any number of factors, such as desired computationalrate, power levels, heat tolerances, processing cycle budget, input datarates, output data rates, memory resources, data bus speeds and otherdesign or performance constraints.

One or more aspects of at least one embodiment may be implemented byrepresentative instructions stored on a machine-readable medium whichrepresents various logic within the processor, which when read by amachine causes the machine to fabricate logic to perform the techniquesdescribed herein. Such representations, known as “IP cores” may bestored on a tangible, machine readable medium and supplied to variouscustomers or manufacturing facilities to load into the fabricationmachines that make the logic or processor. Of note, various embodimentsdescribed herein may, of course, be implemented using any appropriatehardware and/or computing software (e.g., C++, Objective-C, Swift,JAVA®, JAVASCRIPT®, PYTHON®, PERL®, QT, etc.).

In some embodiments, one or more of exemplary inventive computer-basedsystems/platforms, exemplary inventive computer-based devices, and/orexemplary inventive computer-based components of the present disclosuremay include or be incorporated, partially or entirely into at least onepersonal computer (PC), laptop computer, ultra-laptop computer, tablet,touch pad, portable computer, handheld computer, palmtop computer,personal digital assistant (PDA), cellular telephone, combinationcellular telephone/PDA, television, smart device (e.g., smart phone,smart tablet or smart television), mobile internet device (MID),messaging device, data communication device, and so forth.

As used herein, the term “server” should be understood to refer to aservice point which provides processing, database, and communicationfacilities. By way of example, and not limitation, the term “server” canrefer to a single, physical processor with associated communications anddata storage and database facilities, or it can refer to a networked orclustered complex of processors and associated network and storagedevices, as well as operating software and one or more database systemsand application software that support the services provided by theserver. Cloud servers are examples.

In some embodiments, as detailed herein, one or more of exemplaryinventive computer-based systems/platforms, exemplary inventivecomputer-based devices, and/or exemplary inventive computer-basedcomponents of the present disclosure may obtain, manipulate, transfer,store, transform, generate, and/or output any digital object and/or dataunit (e.g., from inside and/or outside of a particular application) thatcan be in any suitable form such as, without limitation, a file, acontact, a task, an email, a tweet, a map, an entire application (e.g.,a calculator), etc. In some embodiments, as detailed herein, one or moreof exemplary inventive computer-based systems/platforms, exemplaryinventive computer-based devices, and/or exemplary inventivecomputer-based components of the present disclosure may be implementedacross one or more of various computer platforms such as, but notlimited to: (1) AMIGAOS™, AMIGAOS4™; (2) FreeBSD, NetBSD, OpenBSD; (3)LINUX®; (4) MICROSOFT WINDOWS®; (5) OpenVMS; (6) OS X (Mac OS); (7)OS/2; (8) SOLARIS®; (9) Tru64 UNIX; (10) VM; (11) ANDROID®; (12) Bada;(13) BLACKBERRY® OS; (14) FIREFOX® OS; (15) iOS; (16) Embedded LINUX®;(17) Palm OS; (18) Symbian; (19) TIZEN®; (20) WebOS; (21) WindowsMobile; (22) Windows Phone; (23) ADOBE® AIR; (24) ADOBE® Flash; (25)ADOBE® Shockwave; (26) Binary Runtime Environment for Wireless (BREW);(27) Cocoa (API); (28) Cocoa Touch; (29) JAVA® Platforms; (30) JavaFX;(31) JavaFX Mobile; (32) Microsoft XNA; (33) Mono; (34) MOZILLA® Prism,XUL and XULRunner; (35) .NET Framework; (36) SILVERLIGHT®; (37) Open WebPlatform; (38) ORACLE® Database; (39) Qt; (40) SAP NETWEAVER®; (41)Smartface; (42) Vexi; and (43) Windows Runtime.

In some embodiments, exemplary inventive computer-basedsystems/platforms, exemplary inventive computer-based devices, and/orexemplary inventive computer-based components of the present disclosuremay be configured to utilize hardwired circuitry that may be used inplace of or in combination with software instructions to implementfeatures consistent with principles of the disclosure. Thus,implementations consistent with principles of the disclosure are notlimited to any specific combination of hardware circuitry and software.For example, various embodiments may be embodied in many different waysas a software component such as, without limitation, a stand-alonesoftware package, a combination of software packages, or it may be asoftware package incorporated as a “tool” in a larger software product.

For example, exemplary software specifically programmed in accordancewith one or more principles of the present disclosure may bedownloadable from a network, for example, a website, as a stand-aloneproduct or as an add-in package for installation in an existing softwareapplication. For example, exemplary software specifically programmed inaccordance with one or more principles of the present disclosure mayalso be available as a client-server software application, or as aweb-enabled software application. For example, exemplary softwarespecifically programmed in accordance with one or more principles of thepresent disclosure may also be embodied as a software package installedon a hardware device.

In some embodiments, exemplary inventive computer-basedsystems/platforms, exemplary inventive computer-based devices, and/orexemplary inventive computer-based components of the present disclosuremay be configured to handle numerous concurrent users that may be, butis not limited to, at least 100 (e.g., but not limited to, 100-999), atleast 1,000 (e.g., but not limited to, 1,000-9,999), at least 10,000(e.g., but not limited to, 10,000-99,999), at least 100,000 (e.g., butnot limited to, 100,000-999,999), at least 1,000,000 (e.g., but notlimited to, 1,000,000-9,999,999), at least 10,000,000 (e.g., but notlimited to, 10,000,000-99,999,999), at least 100,000,000 (e.g., but notlimited to, 100,000,000-999,999,999), at least 1,000,000,000 (e.g., butnot limited to, 1,000,000,000-999,999,999,999), and so on.

In some embodiments, exemplary inventive computer-basedsystems/platforms, exemplary inventive computer-based devices, and/orexemplary inventive computer-based components of the present disclosuremay be configured to output to distinct, specifically programmedgraphical user interface implementations of the present disclosure(e.g., a desktop, a web app., etc.). In various implementations of thepresent disclosure, a final output may be displayed on a displayingscreen which may be, without limitation, a screen of a computer, ascreen of a mobile device, or the like. In various implementations, thedisplay may be a holographic display. In various implementations, thedisplay may be a transparent surface that may receive a visualprojection. Such projections may convey various forms of information,images, and/or objects. For example, such projections may be a visualoverlay for a mobile augmented reality (MAR) application.

In some embodiments, exemplary inventive computer-basedsystems/platforms, exemplary inventive computer-based devices, and/orexemplary inventive computer-based components of the present disclosuremay be configured to be utilized in various applications which mayinclude, but not limited to, gaming, mobile-device games, video chats,video conferences, live video streaming, video streaming and/oraugmented reality applications, mobile-device messenger applications,and others similarly suitable computer-device applications.

As used herein, the term “mobile electronic device,” or the like, mayrefer to any portable electronic device that may or may not be enabledwith location tracking functionality (e.g., MAC address, InternetProtocol (IP) address, or the like). For example, a mobile electronicdevice can include, but is not limited to, a mobile phone, PersonalDigital Assistant (PDA), Blackberry™, Pager, Smartphone, or any otherreasonable mobile electronic device.

As used herein, the terms “cloud,” “Internet cloud,” “cloud computing,”“cloud architecture,” and similar terms correspond to at least one ofthe following: (1) a large number of computers connected through areal-time communication network (e.g., Internet); (2) providing theability to run a program or application on many connected computers(e.g., physical machines, virtual machines (VMs)) at the same time; (3)network-based services, which appear to be provided by real serverhardware, and are in fact served up by virtual hardware (e.g., virtualservers), simulated by software running on one or more real machines(e.g., allowing to be moved around and scaled up (or down) on the flywithout affecting the end user).

In some embodiments, the exemplary inventive computer-basedsystems/platforms, the exemplary inventive computer-based devices,and/or the exemplary inventive computer-based components of the presentdisclosure may be configured to securely store and/or transmit data byutilizing one or more of encryption techniques (e.g., private/public keypair, Triple Data Encryption Standard (3DES), block cipher algorithms(e.g., IDEA, RC2, RC5, CAST and Skipjack), cryptographic hash algorithms(e.g., MD5, RIPEMD-160, RTR0, SHA-1, SHA-2, Tiger (TTH), WHIRLPOOL,RNGs).

The aforementioned examples are, of course, illustrative and notrestrictive.

As used herein, the term “user” shall have a meaning of at least oneuser. In some embodiments, the terms “user”, “subscriber” “consumer” or“customer” should be understood to refer to a user of an application orapplications as described herein and/or a consumer of data supplied by adata provider. By way of example, and not limitation, the terms “user”or “subscriber” can refer to a person who receives data provided by thedata or service provider over the Internet in a browser session, or canrefer to an automated software application which receives the data andstores or processes the data.

FIG. 6 depicts a block diagram of an exemplary computer-basedsystem/platform 400 in accordance with one or more embodiments of thepresent disclosure. However, not all of these components may be requiredto practice one or more embodiments, and variations in the arrangementand type of the components may be made without departing from the spiritor scope of various embodiments of the present disclosure. In someembodiments, the exemplary inventive computing devices and/or theexemplary inventive computing components of the exemplary computer-basedsystem/platform 400 may be configured to manage a large number ofmembers and/or concurrent transactions, as detailed herein. In someembodiments, the exemplary computer-based system/platform 400 may bebased on a scalable computer and/or network architecture thatincorporates varies strategies for assessing the data, caching,searching, and/or database connection pooling. An example of thescalable architecture is an architecture that is capable of operatingmultiple servers.

In some embodiments, referring to FIG. 6 , members 402-404 (e.g.,clients) of the exemplary computer-based system/platform 400 may includevirtually any computing device capable of receiving and sending amessage over a network (e.g., cloud network), such as network 405, toand from another computing device, such as servers 406 and 407, eachother, and the like. In some embodiments, the member devices 402-404 maybe personal computers, multiprocessor systems, microprocessor-based orprogrammable consumer electronics, network PCs, and the like. In someembodiments, one or more member devices within member devices 402-404may include computing devices that typically connect using a wirelesscommunications medium such as cell phones, smart phones, pagers, walkietalkies, radio frequency (RF) devices, infrared (IR) devices, CBs,integrated devices combining one or more of the preceding devices, orvirtually any mobile computing device, and the like. In someembodiments, one or more member devices within member devices 402-404may be devices that are capable of connecting using a wired or wirelesscommunication medium such as a PDA, POCKET PC, wearable computer, alaptop, tablet, desktop computer, a netbook, a video game device, apager, a smart phone, an ultra-mobile personal computer (UMPC), and/orany other device that is equipped to communicate over a wired and/orwireless communication medium (e.g., NFC, RFID, NBIOT, 3G, 4G, 5G, GSM,GPRS, WIFI®, WIMAX®, CDMA, satellite, ZIGBEE®, etc.). In someembodiments, one or more member devices within member devices 402-404may include may run one or more applications, such as Internet browsers,mobile applications, voice calls, video games, videoconferencing, andemail, among others. In some embodiments, one or more member deviceswithin member devices 402-404 may be configured to receive and to sendweb pages, and the like. In some embodiments, an exemplary specificallyprogrammed browser application of the present disclosure may beconfigured to receive and display graphics, text, multimedia, and thelike, employing virtually any web based language, including, but notlimited to Standard Generalized Markup Language (SMGL), such asHyperText Markup Language (HTML), a wireless application protocol (WAP),a Handheld Device Markup Language (HDML), such as Wireless MarkupLanguage (WML), WMLScript, XML, JavaScript, and the like. In someembodiments, a member device within member devices 402-404 may bespecifically programmed by either JAVA®, .Net, QT, C, C++ and/or othersuitable programming language. In some embodiments, one or more memberdevices within member devices 402-404 may be specifically programmedinclude or execute an application to perform a variety of possibletasks, such as, without limitation, messaging functionality, browsing,searching, playing, streaming or displaying various forms of content,including locally stored or uploaded messages, images and/or video,and/or games.

In some embodiments, the exemplary network 405 may provide networkaccess, data transport and/or other services to any computing devicecoupled to it. In some embodiments, the exemplary network 405 mayinclude and implement at least one specialized network architecture thatmay be based at least in part on one or more standards set by, forexample, without limitation, Global System for Mobile communication(GSM) Association, the Internet Engineering Task Force (IETF), and theWorldwide Interoperability for Microwave Access (WiMAX) forum. In someembodiments, the exemplary network 405 may implement one or more of aGSM architecture, a General Packet Radio Service (GPRS) architecture, aUniversal Mobile Telecommunications System (UMTS) architecture, and anevolution of UMTS referred to as Long Term Evolution (LTE). In someembodiments, the exemplary network 405 may include and implement, as analternative or in conjunction with one or more of the above, a WiMAXarchitecture defined by the WiMAX forum. In some embodiments and,optionally, in combination of any embodiment described above or below,the exemplary network 405 may also include, for instance, at least oneof a local area network (LAN), a wide area network (WAN), the Internet,a virtual LAN (VLAN), an enterprise LAN, a layer 3 virtual privatenetwork (VPN), an enterprise IP network, or any combination thereof. Insome embodiments and, optionally, in combination of any embodimentdescribed above or below, at least one computer network communicationover the exemplary network 405 may be transmitted based at least in parton one of more communication modes such as but not limited to: NFC,RFID, Narrow Band Internet of Things (NBIOT), ZIGBEE®, 3G, 4G, 5G, GSM,GPRS, WIFI®, WIMAX®, CDMA, satellite and any combination thereof. Insome embodiments, the exemplary network 405 may also include massstorage, such as network attached storage (NAS), a storage area network(SAN), a content delivery network (CDN) or other forms of computer ormachine readable media.

In some embodiments, the exemplary server 406 or the exemplary server407 may be a web server (or a series of servers) running a networkoperating system, examples of which may include but are not limited toMicrosoft Windows Server, Novell NetWare, or Linux. In some embodiments,the exemplary server 406 or the exemplary server 407 may be used forand/or provide cloud and/or network computing. Although not shown inFIG. 6 , in some embodiments, the exemplary server 406 or the exemplaryserver 407 may have connections to external systems like email, SMSmessaging, text messaging, ad content providers, etc. Any of thefeatures of the exemplary server 406 may be also implemented in theexemplary server 407 and vice versa.

In some embodiments, one or more of the exemplary servers 406 and 407may be specifically programmed to perform, in non-limiting example, asauthentication servers, search servers, email servers, social networkingservices servers, SMS servers, IM servers, MMS servers, exchangeservers, photo-sharing services servers, advertisement providingservers, financial/banking-related services servers, travel servicesservers, or any similarly suitable service-base servers for users of themember computing devices 401-404.

In some embodiments and, optionally, in combination of any embodimentdescribed above or below, for example, one or more exemplary computingmember devices 402-404, the exemplary server 406, and/or the exemplaryserver 407 may include a specifically programmed software module thatmay be configured to send, process, and receive information using ascripting language, a remote procedure call, an email, a tweet, ShortMessage Service (SMS), Multimedia Message Service (MMS), instantmessaging (IM), internet relay chat (IRC), mIRC, JABBER®, an applicationprogramming interface, Simple Object Access Protocol (SOAP) methods,Common Object Request Broker Architecture (CORBA), HTTP (HypertextTransfer Protocol), REST (Representational State Transfer), or anycombination thereof.

FIG. 7 depicts a block diagram of another exemplary computer-basedsystem/platform 500 in accordance with one or more embodiments of thepresent disclosure. However, not all of these components may be requiredto practice one or more embodiments, and variations in the arrangementand type of the components may be made without departing from the spiritor scope of various embodiments of the present disclosure. In someembodiments, the member computing devices 502 a, 502 b thru 502 n showneach at least includes a computer-readable medium, such as arandom-access memory (RAM) 508 coupled to a processor 510 or FLASHmemory. In some embodiments, the processor 510 may executecomputer-executable program instructions stored in memory 508. In someembodiments, the processor 510 may include a microprocessor, an ASIC,and/or a state machine. In some embodiments, the processor 510 mayinclude, or may be in communication with, media, for examplecomputer-readable media, which stores instructions that, when executedby the processor 510, may cause the processor 510 to perform one or moresteps described herein. In some embodiments, examples ofcomputer-readable media may include, but are not limited to, anelectronic, optical, magnetic, or other storage or transmission devicecapable of providing a processor, such as the processor 510 of client502 a, with computer-readable instructions. In some embodiments, otherexamples of suitable media may include, but are not limited to, a floppydisk, CD-ROM, DVD, magnetic disk, memory chip, ROM, RAM, an ASIC, aconfigured processor, all optical media, all magnetic tape or othermagnetic media, or any other medium from which a computer processor canread instructions. Also, various other forms of computer-readable mediamay transmit or carry instructions to a computer, including a router,private or public network, or other transmission device or channel, bothwired and wireless. In some embodiments, the instructions may comprisecode from any computer-programming language, including, for example, C,C++, JAVA®, PYTHON®, PERL®, JAVASCRIPT®, and etc.

In some embodiments, member computing devices 502 a through 502 n mayalso comprise a number of external or internal devices such as a mouse,a CD-ROM, DVD, a physical or virtual keyboard, a display, a speaker, orother input or output devices. In some embodiments, examples of membercomputing devices 502 a through 502 n (e.g., clients) may be any type ofprocessor-based platforms that are connected to a network 506 such as,without limitation, personal computers, digital assistants, personaldigital assistants, smart phones, pagers, digital tablets, laptopcomputers, Internet appliances, and other processor-based devices. Insome embodiments, member computing devices 502 a through 502 n may bespecifically programmed with one or more application programs inaccordance with one or more principles/methodologies detailed herein. Insome embodiments, member computing devices 502 a through 502 n mayoperate on any operating system capable of supporting a browser orbrowser-enabled application, such as Microsoft™ Windows™, and/or Linux.In some embodiments, member computing devices 502 a through 502 n shownmay include, for example, personal computers executing a browserapplication program such as Microsoft Corporation's INTERNET EXPLORER®,Apple Computer, Inc.'s SAFARI®, MOZILLA FIREFOX®, and/or Opera. In someembodiments, through the member computing client devices 502 a through502 n, users, 512 a through 512 n, may communicate over the exemplarynetwork 506 with each other and/or with other systems and/or devicescoupled to the network 506. As shown in FIG. 10 , exemplary serverdevices 504 and 513 may be also coupled to the network 506. In someembodiments, one or more member computing devices 502 a through 502 nmay be mobile clients.

In some embodiments, at least one database of exemplary databases 507and 515 may be any type of database, including a database managed by adatabase management system (DBMS). In some embodiments, an exemplaryDBMS-managed database may be specifically programmed as an engine thatcontrols organization, storage, management, and/or retrieval of data inthe respective database. In some embodiments, the exemplary DBMS-manageddatabase may be specifically programmed to provide the ability to query,backup and replicate, enforce rules, provide security, compute, performchange and access logging, and/or automate optimization. In someembodiments, the exemplary DBMS-managed database may be chosen fromOracle database, IBM DB2, Adaptive Server Enterprise, FILEMAKER®,Microsoft Access, Microsoft SQL Server, MYSQL®, POSTGRESQL®, and a NoSQLimplementation. In some embodiments, the exemplary DBMS-managed databasemay be specifically programmed to define each respective schema of eachdatabase in the exemplary DBMS, according to a particular database modelof the present disclosure which may include a hierarchical model,network model, relational model, object model, or some other suitableorganization that may result in one or more applicable data structuresthat may include fields, records, files, and/or objects. In someembodiments, the exemplary DBMS-managed database may be specificallyprogrammed to include metadata about the data that is stored.

In some embodiments, the exemplary inventive computer-basedsystems/platforms, the exemplary inventive computer-based devices,and/or the exemplary inventive computer-based components of the presentdisclosure may be specifically configured to operate in an cloudcomputing/architecture such as, but not limiting to: infrastructure aservice (IaaS), platform as a service (PaaS), and/or software as aservice (SaaS). FIGS. 8 and 9 illustrate schematics of exemplaryimplementations of the cloud computing/architecture(s) in which theexemplary inventive computer-based systems/platforms, the exemplaryinventive computer-based devices, and/or the exemplary inventivecomputer-based components of the present disclosure may be specificallyconfigured to operate.

In some embodiments, a method may include:

receiving, by a processor, a plurality of bias data categories;

receiving, by the processor, a data input from a user for classificationin a plurality of data categories;

utilizing, by the processor, a classification machine learning model toclassify the data input in at least one data category from the pluralityof data categories and determine a first confidence probability in aclassification outcome;

utilizing, by the processor, a bias filter machine learning model todetermine a second confidence probability that the classificationoutcome of classifying the data input into the at least one datacategory from the plurality of data categories is based on at least onebias characteristic associated with at least one bias data category fromthe plurality of bias data categories;

utilizing, by the processor, a gate machine learning model to determinewhen to output the classification outcome of classifying the data inputinto the at least one data category from the plurality of datacategories to a computing device of a user based at least in part on:

i) the first confidence probability,

ii) the second confidence probability, and

iii) a predefined bias threshold; and

outputting, by the processor, the classification outcome of classifyingthe data input into the at least one data category from the plurality ofdata categories to the computing device of the user based on thedetermination of the gate machine learning model.

In some embodiments, outputting the classification outcome ofclassifying the data input into the at least one data category from theplurality of data categories may include outputting the at least onedata category to the computing device of the user when the gate machinelearning model assesses that the second confidence probability is belowthe predefined bias threshold.

In some embodiments, outputting the classification outcome ofclassifying the data input into the at least one data category from theplurality of data categories may include blocking an output to the userof the at least one data category of the classified data input when thegate machine learning model assesses that the second confidenceprobability is above the predefined bias threshold.

In some embodiments, utilizing the gate machine learning model todetermine when to output the classification outcome may includeidentifying a potential bias in classifying the data input when the gatemachine learning model assesses the first probability is below aclassification confidence threshold and the second probability is abovethe predefined bias threshold.

In some embodiments, outputting the classification outcome may includeoutputting to the user, the at least one data category from classifyingthe data input and a notification that the at least one data category ispotentially biased.

In some embodiments, the method may further include using, by theprocessor, the classified data input and the at least one data categoryidentified as potentially biased to retrain the at least oneclassification machine learning model.

In some embodiments, the data input may be selected from the groupconsisting of an image file, a text file, a video file, and an audiofile.

In some embodiments, the bias filter machine learning model may beselected from the group consisting of a convolutional neural networkmodel, a dilated convolutional neural network model, and a recurrentneural network model.

In some embodiments, the classification machine learning model mayinclude a classification neural network model.

In some embodiments, receiving the plurality of bias data categories mayinclude receiving the plurality of bias data categories from the user.from a regulator, or both.

In some embodiments, a system may include a memory and a processor. Theprocessor may be configured to:

receive a plurality of bias data categories;

receive a data input from a user for classification in a plurality ofdata categories;

utilize a classification machine learning model to classify the datainput in at least one data category from the plurality of datacategories and determine a first confidence probability in aclassification outcome;

utilize a bias filter machine learning model to determine a secondconfidence probability that the classification outcome of classifyingthe data input into the at least one data category from the plurality ofdata categories is based on at least one bias characteristic associatedwith at least one bias data category from the plurality of bias datacategories;

utilize a gate machine learning model to determine when to output theclassification outcome of classifying the data input into the at leastone data category from the plurality of data categories to a computingdevice of a user based at least in part on:

-   i) the first confidence probability,-   ii) the second confidence probability, and-   iii) a predefined bias threshold; and

output the classification outcome of classifying the data input into theat least one data category from the plurality of data categories to thecomputing device of the user based on the determination of the gatemachine learning model.

In some embodiments, the processor may be configured to output theclassification outcome of classifying the data input into the at leastone data category from the plurality of data categories by outputtingthe at least one data category to the computing device of the user whenthe gate machine learning model assesses that the second confidenceprobability is below the predefined bias threshold.

In some embodiments, the processor may be configured to output theclassification outcome of classifying the data input into the at leastone data category from the plurality of data categories by blocking anoutput to the user of the at least one data category of the classifieddata input when the gate machine learning model assesses that the secondconfidence probability is above the predefined bias threshold.

In some embodiments, the processor may be configured to utilize the gatemachine learning model to determine when to output the classificationoutcome by identifying a potential bias in classifying the data inputwhen the gate machine learning model assesses the first probability isbelow a classification confidence threshold and the second probabilityis above the predefined bias threshold.

In some embodiments, the processor may be configured to output theclassification outcome by outputting to the user, the at least one datacategory from classifying the data input and a notification that the atleast one data category is potentially biased.

In some embodiments, the processor may be further configured to use theclassified data input and the at least one data category identified aspotentially biased to retrain the at least one classification machinelearning model.

In some embodiments, the data input may be selected from the groupconsisting of an image file, a text file, a video file, and an audiofile.

In some embodiments, the bias filter machine learning model may beselected from the group consisting of a convolutional neural networkmodel, a dilated convolutional neural network model, and a recurrentneural network model.

In some embodiments, the classification machine learning model mayinclude a classification neural network model.

In some embodiments, the processor may be configured to receive theplurality of bias data categories by receiving the plurality of biasdata categories from the user. from a regulator, or both.

Publications cited throughout this document are hereby incorporated byreference in their entirety. While one or more embodiments of thepresent disclosure have been described, it is understood that theseembodiments are illustrative only, and not restrictive, and that manymodifications may become apparent to those of ordinary skill in the art,including that various embodiments of the inventive methodologies, theinventive systems/platforms, and the inventive devices described hereincan be utilized in any combination with each other. Further still, thevarious steps may be carried out in any desired order (and any desiredsteps may be added and/or any desired steps may be eliminated).

The invention claimed is:
 1. A method, comprising: receiving concurrently into a parallel configuration of a classification machine learning model and a bias filter machine learning model a data input for classification into at least one data category of a plurality of data categories; wherein the data input comprises initial data and the plurality of data categories; generating concurrently a classification output by the classification machine learning model and a bias confidence probability output by the bias filter machine learning model; inputting the classification output and the bias confidence probability output into a gate machine learning model; wherein the classification output comprises: a classification of the initial data in the at least one data category from the plurality of data categories to form a classified data, and a classification confidence probability in the classification of the initial data in the classified data; wherein the bias confidence probability output comprises a bias confidence probability that the classification of the initial data in the classified data is related to at least one bias characteristic; training incrementally the classification, bias filter, and gate machine learning models in an iterative manner with feedback using the data input, the classification output, and the bias confidence probability output; generating a classification outcome of the classified data by the gate machine learning model based on the classification output and the bias confidence probability output; wherein the classification outcome is one of a category of: i) biased ii) potentially biased, or iii) unbiased; retraining, for each of the at least one bias characteristic wherein the classification outcome is biased or potentially biased, for at least one subsequent iteration of the incremental iterative training with feedback of the classification, bias filter, and gate machine learning models to update the classification, bias filter, and gate machine learning models until the bias confidence probability is below a predefined bias threshold by blocking the classified data that is biased and adding into the data input at least: i) the classification output and ii) the bias confidence probability output; and outputting the classification outcome of the classified data to a computing device associated with a user when the classification outcome of the classified data is unbiased or potentially biased and blocking an output of the classified data when it is determined by the classification, bias filter, and gate machine learning models that the classification outcome of the classified data is biased.
 2. The method according to claim 1, wherein the outputting comprises displaying a blocking alert that the classified data is biased, or an indication of potential bias on a display of the computing device associated with the user.
 3. The method according to claim 1, wherein the data input is selected from the group consisting of an image file, a text file, a video file, and an audio file.
 4. The method according to claim 1, wherein the bias filter machine learning model is selected from the group consisting of a convolutional neural network model, a dilated convolutional neural network model, an autoencoder, a variational autoencoder, and a recurrent neural network model.
 5. The method according to claim 1, further comprising providing feedback to the bias filter machine learning model when the data input is misclassified.
 6. The method according to claim 1, wherein the bias filter machine learning model comprises one or more bias characteristics.
 7. The method according to claim 1, wherein the at least one bias characteristic is selected from the group consisting of gender, race, skin color, shape of body part, mental abilities, physical abilities, place of origin, social affiliation, political affiliation, and education level.
 8. A system, comprising: a memory; and a processor configured to: receive concurrently into a parallel configuration of a classification machine learning model and a bias filter machine learning model a data input for classification into at least one data category of a plurality of data categories; wherein the data input comprises initial data and the plurality of data categories; generate concurrently a classification output by the classification machine learning model and a bias confidence probability output by the bias filter machine learning model; input the classification output and the bias confidence probability output into a gate machine learning model; wherein the classification output comprises: a classification of the initial data input in the at least one data category from the plurality of data categories to form a classified data, and a classification confidence probability in the classification of the initial data in the classified data; wherein the bias confidence probability output comprises a bias confidence probability that the classification of the initial data in the classified data is related to at least one bias characteristic; train incrementally the classification, bias filter, and gate machine learning models in an iterative manner with feedback using the data input, the classification output, and the bias confidence probability output; generate a classification outcome of the classified data by the gate machine learning model based on the classification output and the bias confidence probability output; wherein the classification outcome is one of a category of: i) biased ii) potentially biased, or iii) unbiased; retrain, for each of the at least one bias characteristic wherein the classification outcome is biased or potentially biased, for at least one subsequent iteration of the incremental iterative training with feedback of the classification, bias filter, and gate machine learning models to update the classification, bias filter, and gate machine learning models until the bias confidence probability is below a predefined bias threshold by blocking the classified data that is biased and adding into the data input at least: i) the classification output and ii) the bias confidence probability output; and output the classification outcome of the classified data to a computing device associated with a user when the classification outcome of the classified data is unbiased or potentially biased and blocking an output of the classified data when it is determined by the classification, bias filter, and gate machine learning models that the classification outcome of the classified data is biased.
 9. The system according to claim 8, further comprising a display, and wherein the processor is configured to output, a blocking alert that the classified data is biased, or an indication of potential bias on the display of the computing device associated with the user.
 10. The system according to claim 8, wherein the data input is selected from the group consisting of an image file, a text file, a video file, and an audio file.
 11. The system according to claim 8, wherein the bias filter machine learning model is selected from the group consisting of a convolutional neural network model, a dilated convolutional neural network model, an autoencoder, a variational autoencoder, and a recurrent neural network model.
 12. The system according to claim 8, wherein the processor is further configured to provide feedback to the bias filter machine learning model when the data input is misclassified.
 13. The system according to claim 8, wherein the bias filter machine learning model comprises one or more bias characteristics.
 14. The system according to claim 8, wherein the at least one bias characteristic is selected from the group consisting of gender, race, skin color, shape of body part, mental abilities, physical abilities, place of origin, social affiliation, political affiliation, and education level.
 15. The method according to claim 1, wherein the classification machine learning model is a classification neural network model.
 16. The method according to claim 1, wherein the gate machine learning model is a neural network model.
 17. The method according to claim 1, wherein the at least one bias characteristic is associated with at least one bias data category from a plurality of bias data categories; and further comprising receiving the plurality of bias data categories from the user, at least one regulator, at least one regulatory body, or any combination thereof.
 18. The system according to claim 8, wherein the classification machine learning model is a classification neural network model.
 19. The system according to claim 8, wherein the gate machine learning model is a neural network model.
 20. The system according to claim 11, wherein the at least one bias characteristic is associated with at least one bias data category from a plurality of bias data categories; and wherein the processor is further configured to receive the plurality of bias data categories from the user, at least one regulator, at least one regulatory body, or any combination thereof. 